- Authored on a paper in the main track of IJCAI-2024 (A* CORE Rankings, Acceptance Rate: 15%).- Assessed payment fraud risk mitigation strategies via the multi-armed bandit problem (reinforcement learning) implemented using the upper confidence bound algorithm (NetworkX, NumPy, Matplotlib, Jupyter) to model interactions between strategic agents for a real-time payments system supporting credit, debt, and an interbank network. Supported by a grant from J.P. Morgan AI Research.

- Publicized findings to experts in industry and academia through peer-reviewed conferences (WIMIN 2023, JMM 2024, SUMS 2024) and accepted for publication at Reliability Engineering & System Safety (Impact Factor: 9.4, Acceptance Rate: 23%).- Achieved significant effort and time savings for risk assessment (d-FMECA/FMEA) in industrial applications by developing a novel artificial intelligence tool in Python (PyQt5, Pandas, NumPy, Hugging Face) capable of providing real-time and… Show more - Publicized findings to experts in industry and academia through peer-reviewed conferences (WIMIN 2023, JMM 2024, SUMS 2024) and accepted for publication at Reliability Engineering & System Safety (Impact Factor: 9.4, Acceptance Rate: 23%).- Achieved significant effort and time savings for risk assessment (d-FMECA/FMEA) in industrial applications by developing a novel artificial intelligence tool in Python (PyQt5, Pandas, NumPy, Hugging Face) capable of providing real-time and interactive responses to support decision making. Show less

- Selected from 400+ applicants to participate in a highly selective 8-week summer program at Carnegie Mellon University in applied mathematics and mathematical finance.- Further developed mathematical finance theories by conducting research on utility maximization within binomial stock pricing models, improving strategies for optimal investment policies on long time scales using Python (NumPy, Pandas) simulations.- Considered applications for financial risk management and… Show more - Selected from 400+ applicants to participate in a highly selective 8-week summer program at Carnegie Mellon University in applied mathematics and mathematical finance.- Further developed mathematical finance theories by conducting research on utility maximization within binomial stock pricing models, improving strategies for optimal investment policies on long time scales using Python (NumPy, Pandas) simulations.- Considered applications for financial risk management and continued research throughout the academic year. Presented findings at the peer-reviewed conference JMM 2022. Show less

- Spurned industry advancements in embedded AI systems by developing a proprietary C++20 application (OpenCV, Microsoft Azure Kinect SDK, k4a, Qt) utilizing mutex locking/unlocking and the Microsoft Azure Kinect SDK to record transformed sequence Color/IR16/Depth16 data at specific framerates and timescales.- Revamped a proprietary v2 scalable system (NVidia Xavier NX) for managing multiple remote data collection devices (Microsoft Azure Kinect) in C++20 (Qt, X11, JetsonGPIO, loguru… Show more - Spurned industry advancements in embedded AI systems by developing a proprietary C++20 application (OpenCV, Microsoft Azure Kinect SDK, k4a, Qt) utilizing mutex locking/unlocking and the Microsoft Azure Kinect SDK to record transformed sequence Color/IR16/Depth16 data at specific framerates and timescales.- Revamped a proprietary v2 scalable system (NVidia Xavier NX) for managing multiple remote data collection devices (Microsoft Azure Kinect) in C++20 (Qt, X11, JetsonGPIO, loguru, unistd) which increased data transfer efficiency from 10TB/hr to 300+TB/hr.- Wrote and benchmarked (Perf, Valgrind) a multithreaded motion detection C++20 script integrating Azure Kinect sensors with thread management functionality (differential imaging, dynamic thread pool management) to enable efficient (<15ms) detection and optimal resource utilization.- Completed the first installation of the product at the MSU Swine Teaching & Research Center and configured the local network for data transfer for analysis by academic staff. This work has received $125K+ in pre-seed Venture Capital funding. Show less

- Supported policy planning and resource management of government authorities in the Lake Chad region by utilizing probabilistic methods in conjunction with simulated rainfall data to forecast impacts on localized ecological systems valuation with helpful data visualization.- Enhanced public understanding of environmental and economic impacts in the Lake Chad region by developing causal models (Bayesian Linear Modeling, Gibbs sampling, Parameter Estimation) and performing comprehensive… Show more - Supported policy planning and resource management of government authorities in the Lake Chad region by utilizing probabilistic methods in conjunction with simulated rainfall data to forecast impacts on localized ecological systems valuation with helpful data visualization.- Enhanced public understanding of environmental and economic impacts in the Lake Chad region by developing causal models (Bayesian Linear Modeling, Gibbs sampling, Parameter Estimation) and performing comprehensive ecological systems valuation in Python (Numpy, Pandas).- Contributed to ongoing academic discourse on ecological statistics by presenting methodologies and findings at the peer-reviewed conference JMM 2021 using trained communication skills and by beginning work on a publication for submission to PLOS ONE. Work has received national attention from several news and media outlets (Article in ”The Conversation”, Article in Rice University News and Media Relations), as well as significant attention from grant providing agencies as recipients of a grant from the British Academy and the Wolfson Foundation for their study, “Sustainability of Agrarian Societies in the Lake Chad Basin” (Article in The British Academy). Show less

- Assisted in developing a general multivariate probability measure technique applicable in an immense variety of settings (causal inference, analysis of object data, etc.) by building causal inference models in Python (Python Optimal Transport, CVXPY, NumPy, multiprocessing) and performing testing on Medicaid data and Lego block objects (sourced from Kaggle).- Increased departmental awareness of the research project by presenting comprehensive literature reviews, facilitating the… Show more - Assisted in developing a general multivariate probability measure technique applicable in an immense variety of settings (causal inference, analysis of object data, etc.) by building causal inference models in Python (Python Optimal Transport, CVXPY, NumPy, multiprocessing) and performing testing on Medicaid data and Lego block objects (sourced from Kaggle).- Increased departmental awareness of the research project by presenting comprehensive literature reviews, facilitating the advancement of project objectives and understanding, and demonstrating disruptive impacts on econometric methodologies. Show less

- Introduced novel ways of using genetic algorithms to segment images by collaborating on a cross-disciplinary team to publish findings and methodologies, culminating in an oral presentation and main track paper at the IEEE High Performance Extreme Computing Conference.- Improved a high-performance scientific image segmentation tool (SEE-Segment) written in Python (DEAP, SkImage, OpenCV, SCOOP, NumPy, PyTorch, TensorFlow) by implementing parallelization (Ray) for segmentation code… Show more - Introduced novel ways of using genetic algorithms to segment images by collaborating on a cross-disciplinary team to publish findings and methodologies, culminating in an oral presentation and main track paper at the IEEE High Performance Extreme Computing Conference.- Improved a high-performance scientific image segmentation tool (SEE-Segment) written in Python (DEAP, SkImage, OpenCV, SCOOP, NumPy, PyTorch, TensorFlow) by implementing parallelization (Ray) for segmentation code, significantly improving efficiency and processing capability on a high-performance computing cluster.- Assisted in the development of a Kubernetes cluster with a CherryPy frontend where Docker instances of the SEE-Segment software package can communicate with a web server and give researchers access the SEE-Segment software through a web based GUI. Show less